The method can be used very advantageously for image data records in medical imaging, in particular for noise reduction in image data records of tomographic imaging. In the case of imaging methods based on X-radiation, such as computed tomography (CT), the resulting images retain quantum noise because of the finite quantum number. A reduction in this quantum noise by raising the X-ray dose is generally excluded because of the increasing radiation burden to the patient.
In these cases, the noise reduction is usually performed by using image filters. However, this image filtering cannot be allowed to lose any clinically relevant information. In particular, edges and small objects are not to be impaired by the filtering. Linear filters are generally excluded for this reason since, although they do reduce the noise, they also, however, simultaneously smooth beyond edges. As a result, the image sharpness is lessened and the detectability of small objects is reduced.
Consequently, use is made in practice of edge-preserving noise reduction methods. The aim of these known methods is to remove the noise in homogeneous image areas and at the same time to maintain the sharpness of edges and fine structures.
A known approach to edge-preserving noise reduction is wavelet thresholding methods such as are proposed, for example, in D. L. Donoho et al., “Ideal Spatial Adaptation by Wavelet Shrinkage”, Biometica, Vol. 81, pages 425 to 455 (1994). In this case, the image to be denoised is decomposed into its wavelet coefficients. High frequency detail coefficients with an absolute value below a certain threshold are set to zero and coefficients are obtained for this. The difficulty with such a method consists in finding a suitable threshold value, chiefly for images with noise that is locally of different strength and is directional, such as is typical in CT images. If the threshold value is excessively large, this can lead to visible sharpness losses and the removal of small structures. On the other hand, the noise is reduced only unsatisfactorily by an excessively small threshold value.
Another approach to noise reduction in image data records of computed tomography is proposed in DE 102005012654 A1. In this method, two CT image data records of the identical object volume are produced by separate reconstruction of even and odd projections of a computed tomography scan. An attempt to distinguish between structures and noise in the images is made with the aid of correlation analyses of the two image data records. However, random correlations of the noise will also occur at some points, and so the noise cannot be adequately removed there.